Supercritical/dense-phase CO2 pipeline leakage diffusion experiment and hazard distance prediction method
The study of the diffusion characteristics of CO2 leakage in pipelines and the determination of the hazardous distance resulting from such leakage under various working conditions are crucial for identifying the high-consequence zone of industrial CO2 pipelines and analyzing the consequences of acci...
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| Những tác giả chính: | , , , , , , , , , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
KeAi Communications Co. Ltd.
2025-06-01
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| Loạt: | Journal of Pipeline Science and Engineering |
| Những chủ đề: | |
| Truy cập trực tuyến: | http://www.sciencedirect.com/science/article/pii/S2667143324000751 |
| Các nhãn: |
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| Tóm tắt: | The study of the diffusion characteristics of CO2 leakage in pipelines and the determination of the hazardous distance resulting from such leakage under various working conditions are crucial for identifying the high-consequence zone of industrial CO2 pipelines and analyzing the consequences of accidents. Currently, there is yet to be a unified conclusion on the delineation and calculation method of hazard distance. This paper has formulated hazard distance calculation and forecasting methods by combining test verification and model calculation. First, a full-scale pipe burst leakage experiment was carried out based on a self-designed and built CO2 pipe leakage device, the CO2 concentration data was measured, and the CO2 diffusion characteristics were analyzed. Then, the experimental measurement values were compared with the calculation results of the CO2 concentration calculation model to verify the reliability of the model. Furthermore, a hazard distance calculation model was introduced, which utilized bilinear interpolation and took into account time-weighted allowable concentrations. This model accurately determined the hazard distances caused by leaks at 11 specific locations along the pipeline. It was found that the location of the leak point can lead to significant differences in the classification of the hazard distance, so the development of a hazard distance prediction model is necessary. Eventually, a hazard distance prediction model was established based on the PSO-BP neural network. Six variables were selected as input parameters: CO2 temperature, pressure, density, position, and distance from the distance valve chamber. The hazard distance caused by a leak at 125 locations along the pipeline was predicted. The results showed that an increase in the transport distance or a location away from the valve chamber would lead to an increase in the hazard distance. At the same time, a CO2 leak in the supercritical state will not generate a hazard distance. |
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| số ISSN: | 2667-1433 |